利用分位数损耗将安全系数集成到容量预测模型中

Gabriela Molinar, J. Gundlach, W. Stork
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引用次数: 1

摘要

电网的安全性和使用寿命是重中之重。电容量预测的安全系数由几个系统操作员定义,以避免高估。应用这一方法,在预测容量高于当前实际容量的情况下,他们仍有时间做出反应,而不会对线路造成长期损害。安全系数的一个例子是2%的高估率。本文提出并比较了将安全系数纳入容量预测系统的两种可能的解决方案。一种是简单的偏差,它是统计计算出来的,然后作为一个常数添加到所有预测中。第二种方法可以应用于机器学习模型,因为它使用分位数损失函数进行训练,该函数具有预定义的高估阈值作为分位数。使用容量预测研究的标准数据库解释了这两种解决方案。最后,明确了基于分位数损失函数的训练的好处,为更接近系统操作员的要求开辟了一个新的实验领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating a Safety Factor into Ampacity Forecasting Models using Quantile Losses
The safety and the life span of the electrical network have the highest priority. A safety factor for ampacity predictions is defined by several system operators to avoid overestimations. Applying this, they still have time to react without producing long-term damages of the line in case the forecast is higher than the actual current capacity. An example of a safety factor is the 2% overestimation rate. This article presents and compares two possible solutions to include a safety factor into ampacity forecasting systems. One is a simple bias, which is statistically calculated and then added as a constant to all predictions. The second approach can be applied to machine learning models, since it uses a quantile loss function for training, which has a predefined threshold of overestimations as a quantile. Both solutions are explained using a standard database for ampacity forecasting studies. At the end, the benefits of training based on a quantile loss function get clear, which opens a new experimentation field to get nearer to the requirements of system operators.
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